Derivative-enhanced Deep Operator Network
Authors: Yuan Qiu, Nolan Bridges, Peng Chen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments validate the effectiveness of our approach. |
| Researcher Affiliation | Academia | Yuan Qiu, Nolan Bridges, Peng Chen Georgia Institute of Technology, Atlanta, GA 30332 {yuan.qiu, bridges, pchen402}@gatech.edu |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The code for data generation, model training and inference, as well as configurations to reproduce the results in this paper can be found at https://github.com/qy849/DE-Deep ONet. |
| Open Datasets | No | We generate Ntrain = 1500 and Ntest = 500 input-output pairs (m(i), u(i)) for training and testing, respectively. |
| Dataset Splits | No | The paper specifies training and test sets but does not explicitly mention a separate validation set split. |
| Hardware Specification | Yes | Table 4: Wall clock time (seconds/iteration with batch size 8) for training on a single NVIDIA RTX A6000 GPU; Table 2: Wall clock time (in seconds) for data generation on 2 AMD EPYC 7543 32-Core Processors |
| Software Dependencies | No | The paper mentions software like FEniCS [31] and hIPPYlib [28], as well as torch.func.jacrev and torch.vmap (implying PyTorch), but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We train each model for 32768 iterations (with the same batch size 8) using an Adam W optimizer [34] and a Step LR learning rate scheduler (We disable learning rate scheduler for DE-Deep ONet). |